Structure-based discovery of a novel Granzyme A (GZMA) inhibitor for autism spectrum disorder through integrative transcriptomics, virtual screening, and long-timescale molecular dynamics simulations

Granzyme A (GZMA) is a trypsin-like serine protease expressed in cytotoxic lymphocytes that cleaves gasdermin B (GSDMB) to release a pore-forming N-terminal fragment, thereby inducing pyroptosis, a lytic and pro-inflammatory form of programmed cell death. This pathway has been implicated in immune dysregulation reported in a subset of individuals with autism spectrum disorder (ASD). In this study, we reanalyzed the peripheral blood transcriptome dataset GSE26415 using an integrative computational framework comprising outlier-aware quality control, differential expression analysis, functional enrichment profiling, weighted gene co-expression network analysis, and protein-protein interaction network centrality scoring. These orthogonal evidence layers were integrated into a composite prioritization score, in which GZMA ranked first among candidate genes, followed by KLRK1 and KLRB1. Querying the Drug-Gene Interaction Database revealed no approved small-molecule inhibitors targeting GZMA, indicating that it remains pharmacologically undrugged. To explore druggability at the structural level, we performed structure-based virtual screening of a ZINC similarity library against the active site of GZMA (PDB ID: 1ORF). This yielded three drug-like candidates (T1, T2, and T3) with docking scores of -9.3, -9.1, and -8.9 kcal/mol, respectively, compared with -7.4 kcal/mol for the co-crystallized reference ligand (0G6). Each drug-protein complex and apo enzyme was studied in 500 ns molecular dynamics to verify the binding stability and conformational persistence compared to the unliganded protein. Among the screening compounds, T2 was able to occupy the catalytic pocket for the entire simulation, with a stable protein-ligand interaction and one low-energy conformational basin. T1, despite a better docking score, was able to detach from the pocket after around 400 ns, while T3 was able to do so in 50 ns. The overall order of binding persistence was T2 > T1 > T3. Collectively, these results prioritize GZMA as a key ASD-associated immune effector candidate and identify T2 as the most credible lead compound. Importantly, the findings underscore the limitation of docking scores alone and demonstrate that long-timescale molecular dynamics simulations and binding free energy are essential for distinguishing kinetically stable binders from transient docking poses. We note that GZMA was transcriptionally downregulated in peripheral blood; its selection rests on network centrality and on the premise that granzyme pathology is governed by enzymatic activity at tissue sites rather than by circulating transcript abundance, a mechanistic hypothesis to be tested at the activity level.

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PLoS ONE
Published
2026-10-13
DOI
https://doi.org/10.1371/journal.pone.0359103
Primary Topic
Computational Drug Discovery Methods
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article
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article

Structure-based discovery of a novel Granzyme A (GZMA) inhibitor for autism spectrum disorder through integrative transcriptomics, virtual screening, and long-timescale molecular dynamics simulations

Ahmed Altuwaijri, Mohammed Mufadhe Alanazi, Metab Alharbi, Talal Alshammari et al.
PLoS ONE
Computational Drug Discovery Methods
article

Structure-based discovery of a novel Granzyme A (GZMA) inhibitor for autism spectrum disorder through integrative transcriptomics, virtual screening, and long-timescale molecular dynamics simulations

Ahmed Altuwaijri, Mohammed Mufadhe Alanazi, Metab Alharbi, Talal Alshammari, Abdulrahman Alshammari, Othman Al Shabanah, Abdullah F. Alasmari
article en

Abstract

Granzyme A (GZMA) is a trypsin-like serine protease expressed in cytotoxic lymphocytes that cleaves gasdermin B (GSDMB) to release a pore-forming N-terminal fragment, thereby inducing pyroptosis, a lytic and pro-inflammatory form of programmed cell death. This pathway has been implicated in immune dysregulation reported in a subset of individuals with autism spectrum disorder (ASD). In this study, we reanalyzed the peripheral blood transcriptome dataset GSE26415 using an integrative computational framework comprising outlier-aware quality control, differential expression analysis, functional enrichment profiling, weighted gene co-expression network analysis, and protein-protein interaction network centrality scoring. These orthogonal evidence layers were integrated into a composite prioritization score, in which GZMA ranked first among candidate genes, followed by KLRK1 and KLRB1. Querying the Drug-Gene Interaction Database revealed no approved small-molecule inhibitors targeting GZMA, indicating that it remains pharmacologically undrugged. To explore druggability at the structural level, we performed structure-based virtual screening of a ZINC similarity library against the active site of GZMA (PDB ID: 1ORF). This yielded three drug-like candidates (T1, T2, and T3) with docking scores of -9.3, -9.1, and -8.9 kcal/mol, respectively, compared with -7.4 kcal/mol for the co-crystallized reference ligand (0G6). Each drug-protein complex and apo enzyme was studied in 500 ns molecular dynamics to verify the binding stability and conformational persistence compared to the unliganded protein. Among the screening compounds, T2 was able to occupy the catalytic pocket for the entire simulation, with a stable protein-ligand interaction and one low-energy conformational basin. T1, despite a better docking score, was able to detach from the pocket after around 400 ns, while T3 was able to do so in 50 ns. The overall order of binding persistence was T2 > T1 > T3. Collectively, these results prioritize GZMA as a key ASD-associated immune effector candidate and identify T2 as the most credible lead compound. Importantly, the findings underscore the limitation of docking scores alone and demonstrate that long-timescale molecular dynamics simulations and binding free energy are essential for distinguishing kinetically stable binders from transient docking poses. We note that GZMA was transcriptionally downregulated in peripheral blood; its selection rests on network centrality and on the premise that granzyme pathology is governed by enzymatic activity at tissue sites rather than by circulating transcript abundance, a mechanistic hypothesis to be tested at the activity level.

PLoS ONEVol. 21(10)
King Saud University (SA)
King Saud University
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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